Business Breakdowns
· David Plon
[00:00:00] S1: And just on that, I think one thing that's really cool – I mentioned earlier with the way I write prompts as I think about sending an email to someone overseas. The difference with an LLM is that the response is, for all intents and purposes, instant. The real insight, I think, comes from doing exactly what you just described, which is iterate on this. The cost of sending a single query is trivial. Start simple, start adding complexity as it's helpful. It's really like a two-way dance to end up in a spot where the AI really is acting like a useful analyst. And you don't need to wait overnight to get the content to then see how to give feedback. You can give that feedback real-time and adjust things, and that becomes super, super powerful.
· Matt Reustle
[00:00:00] S0: Your prompts are loaded into Portrait for various primers or whatnot, and you see the layers, the levels of detail that go into those, and it's actually quite helpful. It made me feel a little down on myself in terms of what I was prompting prior to seeing those, but I think, yeah, it definitely rings true. Taking it up a level, I have seen quite a few individuals who are getting a lot of value out of AI, but it almost feels like it's siloed. You could have two individuals at the same fund that use entirely different tools, and they're getting value. Obviously, the enterprise or the fund is benefiting from all of it, but it doesn't necessarily feel strategically aligned, or that there’s too much collaboration in regards to it. Have you noticed anything in terms of best practices for funds just to get some type of adoption that's aligned or collaborative?
· David Plon
[00:00:00] S1: Because investing is ultimately – a lot of the research is building conviction, and people build that in different ways. It's challenging to mandate changes in a research process because if I was at my last job and the head of the firm said, "Okay, now you must use only sell-side models for your models," I would really struggle. I can certainly sympathize with that. I know a lot of firms have been trying top-down to say, "Hey, we don't want to fall behind on AI. You need to start using such and such tool." And I think that can sometimes work in certain ways, but in other ways it can backfire and people push back. What I've seen the most adoption and firms that are taking the most advantage of this, I'd say, is finding the right balance between having firm-wide initiatives that don't force people to change their behavior, while letting individuals experiment and figure out where AI is going to be additive, and reducing friction without negatively impacting conviction. So for instance, just using Portrait as an example, there are firms where, at the firm level, we spend a lot of time building idea generation screens that are super bespoke to them and their process, and we just run those like an outsourced analyst. And that's really value-add, because all we're doing is, we're not asking anyone to change the process. They just get a bunch of really interesting pitches that now they can spend time on if they want. And similarly, the thesis monitoring has become a really big thing. And the nice thing about thesis monitoring is it doesn't require a change in process. All we're doing is we take a ticker and maybe your investment thesis, and then we're just pulling in data points that, at least our system believes, could be really helpful and incremental to your thinking. And again, that doesn't require anything new. It just requires looking at a new insight when it arrives and ignoring it if it's not relevant. Whereas I think for the actual day-to-day asking queries, building outputs, it really just depends on the user. And obviously, working with a firm that specializes in this and has built software specifically for this vertical can be really helpful for driving adoption. Because unlike a general tool like ChatGPT, someone building software in their space can customize the UX around the specific workflows.
· David Plon, Matt Reustle
But ultimately, I think that adoption has to happen bottoms-up, and people need to feel comfortable with it to be able to continue making high-quality investment decisions. And to the extent people have changed the process – and many people have – they've done so because they feel trust in it. And I think building that trust has to happen on the individual level. [00:00:00] S0: Yeah, it's actually an interesting perspective that I somehow hadn't thought about, but when it comes to any type of investment research and investment process, people do it all differently within the same fund. They might have the same core things that they look for, but the path to get there is quite different. And even model formats – I can remember many an argument over having a single specified template and people moving off of that. So yeah, I think there's a lot to that, and maybe that opened my eyes a little bit. On the practices that firms or individuals can do in terms of investing in things that maybe aren't obvious in value today. But as the technology evolves, maybe they'll be able to leverage more. And I mentioned before we talked, there's the famous example of the sports analytics teams that had been tracking data well before sports analytics were a thing, and they were only able to really benefit from all those data practices decades into the future. But the teams that never kept up with it, it's really hard to go back in time. So are there exercises or practices that you think funds or individuals can start to implement that will pay off in time?
· David Plon
[00:00:00] S1: Obviously, everything we spoke about earlier with experimentation certainly applies here. But I think one thing that is going to be really important, and already was historically, but I think carries a lot more weight going forward, is just the importance of documentation of thinking and decisions and research. Essentially, these models, the usefulness of them rises exponentially with the amount of context they're given. As the model context length expands and as their agentic reasoning gets more heavily utilized to do longer-running tasks, and they ultimately shift from being a helpful research tool to something that lives within a firm and can execute a research process, their ability to do it is going to be a function of how much data they have on how you and your firm operates. So I think firms where they have documented memos and even write up short paragraphs on why they made a trading decision – it's hard to know ex-ante which data is going to be used and how – but I think it's a pretty reasonable bet that having that data, at a minimum, is helpful for the humans, but will certainly be helpful for the machines. Because I think – a moment ago we were speaking about adoption – what’s challenging is loading in that context. It's the same challenge it goes when you hire a junior analyst: needing to teach them everything about how you think and your past trading history with a given name. And all that information just takes a ton of time to convey. And so to the extent that that data can be captured in real-time, that forms an enormously valuable source of IP that will make AI unique to you. And again, it's a little hard to know upfront how that's going to be used, and it probably takes some creativity to figure it out. But hard to imagine two or three years from now that every piece of data isn't being used within a model that is operating within the context of a fund.
· Matt Reustle
[00:00:00] S0: It's a perfect example. I could speak to on-ramping at places where all they had were the memos, which were the buy and sell decisions for positions, which are valuable in some ways, but they're usually overly polished, versus those that after earnings they would have their quick blurb. Maybe Amazon entered the space and was something to monitor, and then a month later they had a meeting with the management team and it made them feel a little worse off, and then another quarter comes out and it's clear Amazon's impacting them, and the memo might just say "Amazon's movement." But to see the thought process and how the team makes decisions, there was something very valuable in me getting up to speed. Again, I think it goes back to that lens of almost thinking of it like a team member in the same way that you would give them the most ammo to align with how you work and what you do. Going back to some of the quick-hitting questions, thinking about down the road and where we're going. I already brought up context windows. We'll use LLMs again, just thinking about some of the most used tools. When you think about where investors – or where you personally find the context window advantage has the most impact, or how you adjust for the challenge that I think many investors face, which is I want to get as much information in there as possible, but sometimes I'm limited by it. How do you approach that, and how much do you think you should worry about that as an investor?